The number of mobile computing devices in use has increased dramatically over the last decade and continues to increase. Examples of mobile computing devices are mobile telephones, digital cameras, and global positioning system (“GPS”) receivers. According to one study, 60% of the world's population has access to mobile telephones. An increasing number of people use digital cameras and some manufacturers of digital cameras presently have revenues of tens of billions of United States dollars annually. GPS receivers can be employed to identify location; measure speed, or acceleration; and for other purposes. In many cases, all three technologies are featured together in some products. As examples, there are now highly portable digital cameras embedded in mobile telephones and other handheld computing devices. Some mobile phones also have GPS receivers to enable users to find their location, directions to a destination, etc. Some digital cameras have GPS receivers to record where a photo was taken.
Digital cameras are used to capture, store, and share images. Often, the images can be viewed nearly immediately after they are captured, such as on a display device associated with the digital cameras. Once an image is captured, it can be processed by computing devices. Image recognition is one such process that can be used to recognize and identify objects in an image. For example, image recognition techniques can determine whether an image contains a human face, a particular object or shape, etc.
Image recognition can be used to provide additional information about a recognized object. As an example, when an object is recognized by a computing device (e.g., by a phone when a user digitizes scene), the computing device can provide additional information about the recognized object.
Augmented reality is a view of a physical, real-world environment that is enhanced by computing devices to digitally augment visual or auditory information a user observes in the real world. As an example, an augmented reality system can receive scene information from a digital camera and a GPS, identify objects (e.g., people, animals, structures, etc.) in the scene, and provide additional information relating to the identified objects (e.g., names or other information). A user of such a system can take a photo of a scene using a mobile computing device (e.g., a digital camera, a cellular phone, a “smartphone,” etc.) and automatically receive information about one or more objects an augmented reality system recognizes in the photographed (i.e., digitized) scene.
To assist in rapidly identifying objects in digitized scenes, augmented reality systems can use classifications for the objects. As an example, an augmented reality system can receive and classify information from multiple sources. The augmented reality system can receive information, e.g., contextual information, from an object directly (e.g., GPS information indicating the object's present geographic location), from other objects (e.g., the color of apparel presently worn by a person when that person is photographed by a friend who has taken many photos of that person), etc. This information is “classified” by associating such information with the object. Then, when a third party—whether known or unknown to an object—digitizes a scene including the object, the augmented reality system can review the classifications to narrow down objects for analysis, e.g., by limiting consideration to only objects matching the classifications. As an example, the augmented reality system can identify a person near a house as a particular person even without recognizing the facial features of the person. The augmented reality system may do this, e.g., by matching what the person is wearing, the time of day, the shape or approximate dimensions of the person, the location of the person, etc. Thus, augmented reality systems can employ classifications of objects to enhance their ability to identify objects, e.g., by combining recognition and matching techniques.